Code
remotes::install_github("jr-leary7/scLANE")Interpretable scRNA-seq Trajectory DE with scLANE
Introduction
In this tutorial we’ll walk through a basic trajectory differential expression analysis. We’ll use the scLANE R package, which we developed with the goal of providing accurate and biologically interpretable models of expression over pseudotime. At the end are some best-practices recommendations, along with a short list of references we used in developing the method & writing the accompanying manuscript.
Libraries
If you haven’t already, install the development version (currently v0.7.9) of scLANE from the GitHub repository.
Next, we’ll load the packages we need to process, analyze, & visualize our data.
Code
library(dplyr) # data manipulation
library(scLANE) # trajectory DE
library(Seurat) # scRNA-seq tools
library(ggplot2) # plot creation
library(patchwork) # plot combination
library(slingshot) # pseudotime estimation
library(reticulate) # Python interface
library(ComplexHeatmap) # heatmaps
rename <- dplyr::renameHelper Functions
We’ll also define a couple utilities to make our plots cleaner to read & easier to make.
Code
theme_umap <- function(base.size = 12) {
ggplot2::theme(axis.ticks = ggplot2::element_blank(),
axis.text = ggplot2::element_blank(),
plot.subtitle = ggplot2::element_text(face = "italic", size = 9),
plot.caption = ggplot2::element_text(face = "italic", size = 9))
}
guide_umap <- function(key.size = 4) {
ggplot2::guides(color = ggplot2::guide_legend(override.aes = list(size = key.size, alpha = 1)))
}And consistent color palettes will make our plots easier to understand.
Code
palette_cluster <- paletteer::paletteer_d("ggsci::default_jama")
palette_celltype <- paletteer::paletteer_d("ggsci::category20_d3")
palette_heatmap <- paletteer::paletteer_d("wesanderson::Zissou1")Data
We’ll load the well-known pancreatic endocrinogenesis data from Bastidas-Ponce et al (2019), which comes with the scVelo Python library & has been used in several pseudotime inference / RNA velocity method papers as a good benchmark dataset due to the simplicity of the underlying trajectory manifold.
Code
import scvelo as scv
adata = scv.datasets.pancreas()The AnnData object contains data that we’ll need to extract, specifically the counts matrices (stored in AnnData.layers) and the cell-level metadata (which is in AnnData.obs).
Code
adataAnnData object with n_obs × n_vars = 3696 × 27998
obs: 'clusters_coarse', 'clusters', 'S_score', 'G2M_score'
var: 'highly_variable_genes'
uns: 'clusters_coarse_colors', 'clusters_colors', 'day_colors', 'neighbors', 'pca'
obsm: 'X_pca', 'X_umap'
layers: 'spliced', 'unspliced'
obsp: 'distances', 'connectivities'Conversion from Python
The reticulate package allows us to pass the counts matrices & metadata from Python back to R. We’ll use the spliced mRNA counts as our default assay, and also define a new assay containing the total (spliced + unspliced) mRNA in each cell. Lastly, we remove genes with non-zero spliced mRNA in 3 or fewer cells.
Code
spliced_counts = adata.layers['spliced'].toarray()
unspliced_counts = adata.layers['unspliced'].toarray()
Note
While downloading this dataset requires a Python installation as well as the installation of the scVelo Python library (and its dependencies), running scLANE is done purely in R & requires no Python whatsoever.
Code
spliced_counts <- Matrix::Matrix(t(py$spliced_counts), sparse = TRUE)
unspliced_counts <- Matrix::Matrix(t(py$unspliced_counts), sparse = TRUE)
rna_counts <- spliced_counts + unspliced_counts
colnames(rna_counts) <- colnames(spliced_counts) <- colnames(unspliced_counts) <- py$adata$obs_names$to_list()
rownames(rna_counts) <- rownames(spliced_counts) <- rownames(unspliced_counts) <- py$adata$var_names$to_list()
spliced_assay <- CreateAssayObject(counts = spliced_counts)
spliced_assay@key <- "spliced_"
unspliced_assay <- CreateAssayObject(counts = unspliced_counts)
unspliced_assay@key <- "unspliced_"
rna_assay <- CreateAssayObject(counts = rna_counts)
rna_assay@key <- "rna_"
meta_data <- py$adata$obs %>%
mutate(cell_name = rownames(.), .before = 1) %>%
rename(celltype = clusters,
celltype_coarse = clusters_coarse) %>%
mutate(nCount_spliced = colSums(spliced_counts),
nFeature_spliced = colSums(spliced_counts > 0),
nCount_unspliced = colSums(unspliced_counts),
nFeature_unspliced = colSums(unspliced_counts > 0),
nCount_rna = colSums(rna_counts),
nFeature_rna = colSums(rna_counts > 0))
seu <- CreateSeuratObject(counts = spliced_assay,
assay = "spliced",
project = "Mm_Panc_Endo",
meta.data = meta_data)
seu@assays$unspliced <- unspliced_assay
seu@assays$RNA <- rna_assay
seu <- seu[rowSums(seu@assays$spliced) > 3, ]Preprocessing
We preprocess the counts using a typical pipeline with QC, normalization & scaling, dimension reduction, and graph-based clustering via the Leiden algorithm.
Code
seu <- PercentageFeatureSet(seu,
pattern = "^mt-",
col.name = "percent_mito",
assay = "spliced") %>%
PercentageFeatureSet(pattern = "^Rp[sl]",
col.name = "percent_ribo",
assay = "spliced") %>%
NormalizeData(assay = "spliced", verbose = FALSE) %>%
NormalizeData(assay = "unspliced", verbose = FALSE) %>%
NormalizeData(assay = "RNA", verbose = FALSE) %>%
FindVariableFeatures(assay = "spliced",
nfeatures = 3000,
verbose = FALSE) %>%
ScaleData(assay = "spliced",
vars.to.regress = c("percent_mito", "percent_ribo"),
model.use = "poisson",
verbose = FALSE) %>%
RunPCA(assay = "spliced",
npcs = 30,
approx = TRUE,
seed.use = 312,
verbose = FALSE) %>%
RunUMAP(reduction = "pca",
dims = 1:30,
n.components = 2,
metric = "cosine",
seed.use = 312,
verbose = FALSE) %>%
FindNeighbors(reduction = "pca",
k.param = 30,
nn.method = "annoy",
annoy.metric = "cosine",
verbose = FALSE) %>%
FindClusters(algorithm = 4,
method = "igraph",
resolution = 0.5,
random.seed = 312,
verbose = FALSE)Let’s visualize the results on our UMAP embedding. The clustering generally agrees with the celltype labels, though there is some overclustering in the ductal cells & underclustering in the mature endocrine celltypes.
Code
p0 <- Embeddings(seu, "umap") %>%
as.data.frame() %>%
magrittr::set_colnames(c("UMAP_1", "UMAP_2")) %>%
mutate(leiden = seu$seurat_clusters) %>%
ggplot(aes(x = UMAP_1, y = UMAP_2, color = leiden)) +
geom_point(size = 1.5,
alpha = 0.75,
stroke = 0) +
scale_color_manual(values = palette_cluster) +
labs(color = "Leiden Cluster") +
theme_scLANE() +
theme_umap() +
theme(plot.title = element_blank(),
axis.title = element_blank(),
axis.line.x = element_blank()) +
guide_umap()
p1 <- Embeddings(seu, "umap") %>%
as.data.frame() %>%
magrittr::set_colnames(c("UMAP_1", "UMAP_2")) %>%
mutate(celltype = seu$celltype) %>%
ggplot(aes(x = UMAP_1, y = UMAP_2, color = celltype)) +
geom_point(size = 1.5,
alpha = 0.75,
stroke = 0) +
scale_color_manual(values = palette_celltype) +
labs(x = "UMAP 1",
y = "UMAP 2",
color = "Celltype") +
theme_scLANE() +
theme_umap() +
theme(plot.title = element_blank()) +
guide_umap()
p2 <- (p0 / p1) +
plot_layout(guides = "collect")
p2
Trajectory Inference
Pseudotime Estimation
We’ll start by fitting a trajectory using the slingshot R package. We define cluster 5 as the starting cluster, since in this case we’re already aware of the dataset’s underlying biology. After generating the estimates for each cell, we rescale the ordering to be defined on \([0, 1]\). This has no effect on the trajectory DE results however, and is mostly an aesthetic choice.
Code
sling_res <- slingshot(Embeddings(seu, "umap"),
start.clus = "5",
clusterLabels = seu$seurat_clusters,
approx_points = 500)
sling_pt <- slingPseudotime(sling_res) %>%
as.data.frame() %>%
magrittr::set_colnames(c("PT")) %>%
mutate(PT = (PT - min(PT)) / (max(PT) - min(PT)))
seu <- AddMetaData(seu,
metadata = sling_pt,
col.name = "sling_pt")Let’s visualize the results on our UMAP embedding. They match what we would expect (knowing the biological background of the data), with ductal cells at the start of the process and endocrine celltypes such as alpha, beta, & delta cells at the end of it.
Code
p3 <- Embeddings(seu, "umap") %>%
as.data.frame() %>%
magrittr::set_colnames(c("UMAP_1", "UMAP_2")) %>%
mutate(PT = sling_pt$PT) %>%
ggplot(aes(x = UMAP_1, y = UMAP_2, color = PT)) +
geom_point(size = 1.5,
alpha = 0.75,
stroke = 0) +
labs(color = "Pseudotime") +
scale_color_gradientn(colors = palette_heatmap,
labels = scales::label_number(accuracy = 0.01)) +
theme_scLANE() +
theme_umap() +
theme(axis.title = element_blank(),
axis.line.x = element_blank())
p4 <- (p3 / p1) +
plot_layout(guides = "collect")
p4
Trajectory Differential Expression
Next, we prepare the primary inputs to scLANE: our Seurat object with the spliced counts set as the default assay, a dataframe containing our estimated pseudotime ordering, a vector of size factors to use as an offset in each model, and a set of genes whose dynamics we want to model. scLANE parallelizes over genes in order to speed up the computation at the expense of using a little more memory. The models are fit using NB GLMs with optimal spline knots identified empirically, and differential expression is quantified using a likelihood ratio test of the fitted model vs. a constant (intercept-only) model. In practice, genes designated as HVGs are usually the best candidates for modeling, so we choose the top 3,000 HVGs as our input.
Note
The testing of the HVG set on its own is also justified by the reality that almost all trajectories are inferred using some sort of dimension-reduced space, and those embeddings are nearly universally generated using a set of HVGs. As such, genes not included in the HVG set actually have no direct relationship with the estimated trajectory, & it’s generally safe to exclude them from trajectory analyses.
Code
top3k_hvg <- HVFInfo(seu) %>%
arrange(desc(variance.standardized)) %>%
slice_head(n = 3000) %>%
rownames(.)
cell_offset <- createCellOffset(seu)
scLANE_models <- testDynamic(seu,
pt = sling_pt,
genes = top3k_hvg,
size.factor.offset = cell_offset,
n.cores = 5,
verbose = FALSE)Registered S3 method overwritten by 'bit':
method from
print.ri gamlssscLANE testing completed for 3000 genes across 1 lineage in 19.018 minsAfter tidying the TDE results with getResultsDE(), we pull a sample of 6 genes from the results & display their test statistics. By default, any gene with an adjusted p-value less than 0.01 is predicted to be dynamic, though this threshold can be easily adjusted.
Code
scLANE_res_tidy <- getResultsDE(scLANE_models)
select(scLANE_res_tidy,
Gene,
Test_Stat,
P_Val,
P_Val_Adj,
Gene_Dynamic_Overall) %>%
mutate(Gene_Dynamic_Overall = if_else(Gene_Dynamic_Overall == 1, "Dynamic", "Static")) %>%
with_groups(Gene_Dynamic_Overall,
slice_sample,
n = 3) %>%
kableExtra::kbl(digits = 4,
booktabs = TRUE,
col.names = c("Gene", "LRT stat.", "P-value", "Adj. p-value", "Predicted gene status")) %>%
kableExtra::kable_classic(full_width = FALSE, "hover")| Gene | LRT stat. | P-value | Adj. p-value | Predicted gene status |
|---|---|---|---|---|
| Pnmal1 | 1001.6104 | 0.0000 | 0.0000 | Dynamic |
| Slc5a9 | 34.1364 | 0.0000 | 0.0000 | Dynamic |
| Aldh5a1 | 31.4081 | 0.0000 | 0.0001 | Dynamic |
| Cela3b | 1.2820 | 0.5268 | 1.0000 | Static |
| A630076J17Rik | NA | NA | NA | Static |
| Tmem119 | 15.7625 | 0.0004 | 0.1182 | Static |
Next, we can use the plotModels() function to visualize the fitted models from scLANE and compare them to other modeling methods. The gene Neurog3 is strongly associated with epithelial cell differentiation, and indeed we see a clear, nonlinear transcriptional dynamic across pseudotime for that gene. A traditional GLM fails to capture that nonlinearity, and while a GAM fits the trend smoothly, it seems to overfit the dynamics near the boundaries of pseudotime - a known issue with additive models. Only the scLANE model accurately models the rapid upregulation and equally swift downregulation of Neurog3 over pseudotime, in addition to identifying the cutpoint in pseudotime at which downregulation begins.
Code
p5 <- plotModels(scLANE_models,
gene = "Neurog3",
pt = sling_pt,
expr.mat = seu,
size.factor.offset = cell_offset,
plot.glm = TRUE,
plot.gam = TRUE) +
scale_color_manual(values = c("forestgreen"))
p5
Downstream analysis
Gene dynamics plots
Using the getFittedValues() function allows us to generate predictions from the models we fit, which we then use to visualize the dynamics of a few genes that are known to be strongly associated with the differentiation of immature progenitors into mature endocrine phenotypes (source 1, source 2). For all four genes, the fitted models show knots chosen in the area of pseudotime around the pre-endocrine cells. This tells us that these driver genes are being upregulated in precursor celltypes & are driving differentiation into the mature celltypes such as alpha & beta cells, after which the genes are downregulated.
Code
p6 <- getFittedValues(scLANE_models,
genes = c("Chga", "Chgb", "Fev", "Cck"),
pt = sling_pt,
expr.mat = seu,
size.factor.offset = cell_offset,
cell.meta.data = select(seu@meta.data, celltype, celltype_coarse)) %>%
ggplot(aes(x = pt, y = rna_log1p)) +
facet_wrap(~gene,
ncol = 2,
scales = "free_y") +
geom_point(aes(color = celltype),
size = 2,
alpha = 0.75,
stroke = 0) +
geom_vline(data = data.frame(gene = "Chga", knot = unique(scLANE_models$Chga$Lineage_A$MARGE_Slope_Data$Breakpoint)),
mapping = aes(xintercept = knot),
linetype = "dashed",
color = "grey20") +
geom_vline(data = data.frame(gene = "Chgb", knot = unique(scLANE_models$Chgb$Lineage_A$MARGE_Slope_Data$Breakpoint)),
mapping = aes(xintercept = knot),
linetype = "dashed",
color = "grey20") +
geom_vline(data = data.frame(gene = "Cck", knot = unique(scLANE_models$Cck$Lineage_A$MARGE_Slope_Data$Breakpoint)),
mapping = aes(xintercept = knot),
linetype = "dashed",
color = "grey20") +
geom_vline(data = data.frame(gene = "Fev", knot = unique(scLANE_models$Fev$Lineage_A$MARGE_Slope_Data$Breakpoint)),
mapping = aes(xintercept = knot),
linetype = "dashed",
color = "grey20") +
geom_ribbon(aes(ymin = scLANE_ci_ll_log1p, ymax = scLANE_ci_ul_log1p),
linewidth = 0,
fill = "grey70",
alpha = 0.9) +
geom_line(aes(y = scLANE_pred_log1p),
color = "black",
linewidth = 0.75) +
scale_x_continuous(labels = scales::label_number(accuracy = 0.01)) +
scale_color_manual(values = palette_celltype) +
labs(x = "Pseudotime",
y = "Normalized Expression") +
theme_scLANE() +
theme(legend.title = element_blank(),
strip.text.x = element_text(face = "italic")) +
guide_umap()
p6
On the other hand, if we use additive models the “peak” of expression is placed among the mature endocrine celltypes - which doesn’t make biological sense if we know that these genes are driving that process of differentiation. This can of course be tweaked by changing the degree or degrees of freedom of the underlying basis spline, but choosing a “best” value for those hyperparameters can be difficult, whereas scLANE identifies optimal parameters internally by default. In addition, the knots chosen by scLANE for each gene can be informative with respect to the underlying biology, whereas the knots from GAMs are evenly spaced at quantiles & carry no biological interpretation.
Code
p7 <- getFittedValues(scLANE_models,
genes = c("Chga", "Chgb", "Fev", "Cck"),
pt = sling_pt,
expr.mat = seu,
size.factor.offset = cell_offset,
cell.meta.data = select(seu@meta.data, celltype, celltype_coarse)) %>%
mutate(rna_raw = rna / size_factor, .before = 7) %>%
with_groups(gene,
mutate,
GAM_fitted_link = predict(nbGAM(expr = rna_raw,
pt = sling_pt,
Y.offset = cell_offset,
spline.df = 3)),
GAM_se_link = predict(nbGAM(expr = rna_raw,
pt = sling_pt,
Y.offset = cell_offset,
spline.df = 3), se.fit = T)[[2]]) %>%
mutate(GAM_pred = exp(GAM_fitted_link) * cell_offset,
GAM_ci_ll = exp(GAM_fitted_link - qnorm(0.975) * GAM_se_link) * cell_offset,
GAM_ci_ul = exp(GAM_fitted_link + qnorm(0.975) * GAM_se_link) * cell_offset,
GAM_pred_log1p = log1p(GAM_pred),
GAM_ci_ll_log1p = log1p(GAM_ci_ll),
GAM_ci_ul_log1p = log1p(GAM_ci_ul)) %>%
ggplot(aes(x = pt, y = rna_log1p)) +
facet_wrap(~gene,
ncol = 2,
scales = "free_y") +
geom_point(aes(color = celltype),
size = 2,
alpha = 0.75,
stroke = 0) +
geom_ribbon(aes(ymin = GAM_ci_ll_log1p, ymax = GAM_ci_ul_log1p),
linewidth = 0,
fill = "grey70",
alpha = 0.9) +
geom_line(aes(y = GAM_pred_log1p),
color = "black",
linewidth = 0.75) +
scale_x_continuous(labels = scales::label_number(accuracy = 0.01)) +
scale_color_manual(values = palette_celltype) +
labs(x = "Pseudotime",
y = "Normalized Expression") +
theme_scLANE() +
theme(legend.title = element_blank(),
strip.text.x = element_text(face = "italic")) +
guide_umap()
p7
Distribution of knot locations
Let’s take a broader view of the dataset by examining the distribution of adaptively chosen knots from our models. We limit the analysis to the set of genes determined to be dynamic.
Code
dyn_genes <- filter(scLANE_res_tidy, Gene_Dynamic_Overall == 1) %>%
pull(Gene)
knot_df <- getKnotDist(scLANE_models, dyn_genes)We’ll plot a histogram of the knot values along with a ridgeplot of the pseudotime distribution for each celltype. We see that a large number of the selected knots are placed at the beginning of the trajectory, around where the ductal cells transition into endocrine progenitors. A smaller set of knots is placed about halfway through the trajectory, which we’ve annotated as the point at which pre-endocrine cells begin differentiating into mature endocrine phenotypes.
Code
p8 <- ggplot(knot_df, aes(x = knot)) +
geom_histogram(aes(y = after_stat(density)),
color = "black",
fill = "white",
linewidth = 0.5) +
geom_density(fill = "deepskyblue3",
alpha = 0.5,
color = "deepskyblue4",
linewidth = 1) +
scale_x_continuous(limits = c(0, 1), labels = scales::label_number(accuracy = 0.01)) +
theme_scLANE() +
theme(axis.title = element_blank(),
axis.text = element_blank(),
axis.ticks.y = element_blank())
p9 <- data.frame(celltype = seu$celltype,
pt = seu$sling_pt) %>%
ggplot(aes(x = pt, y = celltype, fill = celltype, color = celltype)) +
ggridges::geom_density_ridges(alpha = 0.75, size = 1, scale = 0.95) +
scale_x_continuous(labels = scales::label_number(accuracy = 0.01), limits = c(0, 1)) +
scale_fill_manual(values = palette_celltype) +
scale_color_manual(values = palette_celltype) +
labs(x = "Pseudotime") +
theme_scLANE() +
theme(axis.title.y = element_blank(),
legend.title = element_blank()) +
guide_umap()
p10 <- (p8 / p9) +
plot_layout(heights = c(1/4, 3/4))
p10
Dynamic gene clustering
We can extract a matrix of fitted values using smoothedCountsMatrix(); here we focus on the top 2,000 most dynamic genes, with the goal of identifying clusters of similarly-expressed genes. Next, the embedGenes() function reduces dimensionality with PCA, clusters the genes using the Leiden algorithm, & embeds the genes in two dimensions with UMAP.
Code
smoothed_counts <- smoothedCountsMatrix(scLANE_models,
pt = sling_pt,
genes = dyn_genes[1:2000],
size.factor.offset = cell_offset,
n.cores = 2L)
gene_embed <- embedGenes(log1p(smoothed_counts$Lineage_A), resolution.param = 0.2)First we’ll visualize the gene clusters on the first two PCs.
Code
p11 <- ggplot(gene_embed, aes(x = pc1, y = pc2, color = leiden)) +
geom_point(size = 2, alpha = 0.75, stroke = 0) +
labs(x = "PC 1",
y = "PC 2",
color = "Leiden Cluster") +
paletteer::scale_color_paletteer_d("ggsci::default_igv") +
theme_scLANE() +
theme_umap() +
guide_umap()
p11
The UMAP embedding shows that even with the relatively small number of genes, clear patterns are visible.
Code
p12 <- ggplot(gene_embed, aes(x = umap1, y = umap2, color = leiden)) +
geom_point(size = 2, alpha = 0.75, stroke = 0) +
labs(x = "UMAP 1",
y = "UMAP 2",
color = "Gene Cluster") +
paletteer::scale_color_paletteer_d("ggsci::default_igv") +
theme_scLANE() +
theme_umap() +
guide_umap()
p12
Expression cascades
We can also plot a heatmap of the dynamic genes; this requires a bit of setup, for which we’ll use the ComplexHeatmap package. We scale each gene, and clip values to be on \([-6, 6]\). The columns (cells) of the heatmap are ordered by estimated pseudotime, and the rows (genes) are ordered by expression peak.
Code
col_anno_df <- select(seu@meta.data,
cell_name,
celltype,
sling_pt) %>%
mutate(celltype = as.factor(celltype)) %>%
arrange(sling_pt)
gene_order <- sortGenesHeatmap(smoothed_counts$Lineage_A, pt.vec = sling_pt$PT)
heatmap_mat <- t(scale(smoothed_counts$Lineage_A))
heatmap_mat[heatmap_mat > 6] <- 6
heatmap_mat[heatmap_mat < -6] <- -6
colnames(heatmap_mat) <- seu$cell_name
heatmap_mat <- heatmap_mat[, col_anno_df$cell_name]
heatmap_mat <- heatmap_mat[gene_order, ]
palette_celltype_hm <- as.character(palette_celltype[1:length(unique(seu$celltype))])
names(palette_celltype_hm) <- levels(col_anno_df$celltype)
col_anno <- HeatmapAnnotation(Celltype = col_anno_df$celltype,
Pseudotime = col_anno_df$sling_pt,
col = list(Celltype = palette_celltype_hm,
Pseudotime = circlize::colorRamp2(seq(0, 1, by = 0.25), palette_heatmap)),
show_legend = TRUE,
show_annotation_name = FALSE,
gap = unit(1, "mm"),
border = TRUE)
palette_cluster_hm <- as.character(paletteer::paletteer_d("ggsci::default_igv")[1:length(unique(gene_embed$leiden))])
names(palette_cluster_hm) <- as.character(unique(gene_embed$leiden))
row_anno <- HeatmapAnnotation(Cluster = as.factor(gene_embed$leiden),
col = list(Cluster = palette_cluster_hm),
show_legend = TRUE,
show_annotation_name = FALSE,
annotation_legend_param = list(title = "Gene\nCluster"),
gap = unit(1, "mm"),
border = TRUE,
which = "row")The heatmap shows clear dynamic patterns across pseudotime; these patterns are often referred to as expression cascades, and represent periodic up- and down-regulation of different gene programs during the course of the underlying biological process.
Code
Heatmap(matrix = heatmap_mat,
name = "Spliced\nmRNA",
col = circlize::colorRamp2(colors = viridis::inferno(50),
breaks = seq(min(heatmap_mat), max(heatmap_mat), length.out = 50)),
cluster_columns = FALSE,
width = 12,
height = 6,
column_title = "",
cluster_rows = FALSE,
top_annotation = col_anno,
left_annotation = row_anno,
show_column_names = FALSE,
show_row_names = FALSE,
use_raster = TRUE,
raster_by_magick = TRUE,
raster_quality = 5)Loading required namespace: magick
Enrichment analysis & gene programs
Using our gene clusters & the gprofiler2 package, we run an enrichment analysis against the biological process (BP) set of gene ontologies. We make sure to order the genes in each cluster by their test statistics by joining to the results table from scLANE.
Code
gene_clust_list <- purrr::map(unique(gene_embed$leiden), \(x) {
filter(gene_embed, leiden == x) %>%
inner_join(scLANE_res_tidy, by = c("gene" = "Gene")) %>%
arrange(desc(Test_Stat)) %>%
pull(gene)
})
names(gene_clust_list) <- paste0("Leiden_", unique(gene_embed$leiden))
enrich_res <- gprofiler2::gost(gene_clust_list,
organism = "mmusculus",
ordered_query = TRUE,
multi_query = FALSE,
sources = "GO:BP",
significant = TRUE)A look at the top 5 most-significant GO terms for each gene cluster reveals heterogeneous functionalities across groups of genes.
Code
mutate(enrich_res$result,
query = gsub("Leiden_", "", query)) %>%
rename(cluster = query) %>%
with_groups(cluster,
slice_head,
n = 5) %>%
select(cluster, term_name, p_value, term_size, query_size, intersection_size, term_id) %>%
kableExtra::kbl(digits = 3,
booktabs = TRUE,
col.names = c("Gene Cluster", "Term Name", "Adj. P-value", "Term Size",
"Query Size", "Intersection Size", "Term ID")) %>%
kableExtra::kable_classic(c("hover"), full_width = FALSE)| Gene Cluster | Term Name | Adj. P-value | Term Size | Query Size | Intersection Size | Term ID |
|---|---|---|---|---|---|---|
| 0 | system development | 0 | 4076 | 654 | 235 | GO:0048731 |
| 0 | multicellular organism development | 0 | 4828 | 714 | 276 | GO:0007275 |
| 0 | anatomical structure development | 0 | 6222 | 714 | 317 | GO:0048856 |
| 0 | developmental process | 0 | 6808 | 714 | 332 | GO:0032502 |
| 0 | animal organ development | 0 | 3278 | 713 | 204 | GO:0048513 |
| 1 | export from cell | 0 | 977 | 396 | 64 | GO:0140352 |
| 1 | secretion by cell | 0 | 908 | 333 | 56 | GO:0032940 |
| 1 | peptide hormone secretion | 0 | 313 | 362 | 36 | GO:0030072 |
| 1 | secretion | 0 | 1084 | 333 | 60 | GO:0046903 |
| 1 | peptide secretion | 0 | 320 | 362 | 36 | GO:0002790 |
| 2 | regulation of secretion by cell | 0 | 672 | 89 | 20 | GO:1903530 |
| 2 | regulation of secretion | 0 | 767 | 89 | 20 | GO:0051046 |
| 2 | response to hexose | 0 | 248 | 89 | 13 | GO:0009746 |
| 2 | response to monosaccharide | 0 | 257 | 89 | 13 | GO:0034284 |
| 2 | regulation of protein secretion | 0 | 326 | 89 | 14 | GO:0050708 |
| 3 | multicellular organism development | 0 | 4828 | 307 | 122 | GO:0007275 |
| 3 | system development | 0 | 4076 | 307 | 109 | GO:0048731 |
| 3 | developmental process | 0 | 6808 | 307 | 147 | GO:0032502 |
| 3 | anatomical structure development | 0 | 6222 | 307 | 138 | GO:0048856 |
| 3 | cell differentiation | 0 | 4737 | 307 | 115 | GO:0030154 |
| 4 | cell cycle | 0 | 1802 | 303 | 168 | GO:0007049 |
| 4 | cell cycle process | 0 | 1240 | 282 | 142 | GO:0022402 |
| 4 | mitotic cell cycle | 0 | 870 | 303 | 122 | GO:0000278 |
| 4 | mitotic cell cycle process | 0 | 730 | 282 | 111 | GO:1903047 |
| 4 | chromosome segregation | 0 | 397 | 260 | 82 | GO:0007059 |
We can use the geneProgramScoring() function to add module scores for each gene cluster to our Seurat object.
Code
program_labels <- c("organogenesis",
"peptide production",
"regulation of organogenesis",
"differentiation",
"cell cycle")
seu <- geneProgramScoring(seu,
genes = gene_embed$gene,
gene.clusters = gene_embed$leiden,
program.labels = program_labels)Visualizing the scores on our UMAP embedding shows us that the peptide program is highly-enriched only in mature endocrine cells. This makes sense biologically as mature endocrine celltypes’ primary roles are to produce peptides such as glucagon (alpha cells), insulin (beta cells), somatostatin (ductal cells), and pancreatic polypeptide (gamma cells).
Code
p13 <- Embeddings(seu, "umap") %>%
as.data.frame() %>%
magrittr::set_colnames(c("UMAP_1", "UMAP_2")) %>%
mutate(peptide_program_score = seu$peptide_production) %>%
ggplot(aes(x = UMAP_1, y = UMAP_2, color = peptide_program_score)) +
geom_point(size = 1.5, alpha = 0.75, stroke = 0) +
labs(color = "Program Score") +
scale_color_gradientn(colors = palette_heatmap,
labels = scales::label_number(accuracy = 0.1)) +
theme_scLANE() +
theme_umap() +
theme(axis.title = element_blank(),
axis.line.x = element_blank())
p14 <- (p13 / p1) +
plot_layout(guides = "collect")
p14
We can also visualize the trend in the peptide program scores over time, which confirms the biological conclusions we came to by inspecting the UMAP.
Code
p15 <- data.frame(PT = sling_pt$PT,
peptide_program_score = seu$peptide_production,
celltype = seu$celltype) %>%
ggplot(aes(x = PT, y = peptide_program_score, color = celltype)) +
geom_point(alpha = 0.75,
stroke = 0,
size = 2) +
geom_smooth(color = "black",
method = "loess",
linewidth = 0.75) +
scale_color_manual(values = palette_celltype) +
labs(x = "Pseudotime", y = "Peptide Program Score") +
theme_scLANE() +
theme(legend.title = element_blank()) +
guide_umap()
p15
Next, in order to identify which genes are “drivers” of a certain gene program, we can use the geneProgramDrivers() function to correlate normalized expression with program scores.
Code
program_drivers <- geneProgramDrivers(seu,
genes = gene_embed$gene,
gene.program = seu$peptide_production,
verbose = FALSE)We display the top 10 most-correlated genes here. For example, retinol-binding protein 4 (Rbp4) is a known marker of pancreatic islet cells, and of delta cells more specifically (source).
Code
slice_head(program_drivers, n = 10) %>%
kableExtra::kbl(digits = 3,
booktabs = TRUE,
row.names = FALSE,
col.names = c("Gene", "Correlation", "P-value", "Adj. P-value")) %>%
kableExtra::kable_classic(c("hover"), full_width = FALSE)| Gene | Correlation | P-value | Adj. P-value |
|---|---|---|---|
| Cpe | 0.833 | 0 | 0 |
| Fam183b | 0.818 | 0 | 0 |
| Gnas | 0.814 | 0 | 0 |
| Pcsk1n | 0.814 | 0 | 0 |
| Rbp4 | 0.771 | 0 | 0 |
| 1700086L19Rik | 0.769 | 0 | 0 |
| Scgn | 0.762 | 0 | 0 |
| Sparc | -0.757 | 0 | 0 |
| Isl1 | 0.756 | 0 | 0 |
| Scg3 | 0.744 | 0 | 0 |
Indeed, normalized expression of Rbp4 is high across all mature endocrine celltypes, with delta cells showing the highest overall mean expression.
Code
p16 <- data.frame(celltype = seu$celltype,
rna = seu@assays$spliced@data["Rbp4", ]) %>%
ggplot(aes(x = celltype, y = rna, color = celltype)) +
ggbeeswarm::geom_quasirandom(alpha = 0.75,
size = 2,
stroke = 0,
show.legend = FALSE) +
scale_color_manual(values = palette_celltype) +
stat_summary(fun = "mean",
geom = "point",
color = "black",
size = 3) +
labs(y = "Normalized Expression") +
theme_scLANE() +
theme(axis.title.x = element_blank())
p16
Conclusions
Hopefully this vignette has been a useful introduction to running the scLANE software and using its outputs to help better understand biology at single-cell resolution. If you have questions about how the models work or are interpreted, software issues, or simply want to compare results feel free to open an issue on the GitHub repository or reach out via email to j.leary@ufl.edu.
References
Bastidas-Ponce, Aimée et al. Comprehensive single cell mRNA profiling reveals a detailed roadmap for pancreatic endocrinogenesis. Development (2019).
Street, Kelly et al. Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics. BMC Genomics (2018).
Stoklosa, Jakub & David Warton. A generalized estimating equation approach to multivariate adaptive regression splines. Journal of Computational and Graphical Statistics (2018).
Session Info
Code
sessioninfo::session_info()─ Session info ───────────────────────────────────────────────────────────────
setting value
version R version 4.3.2 (2023-10-31)
os macOS Sonoma 14.1.2
system x86_64, darwin20
ui X11
language (EN)
collate en_US.UTF-8
ctype en_US.UTF-8
tz America/New_York
date 2024-01-04
pandoc 3.1.9 @ /usr/local/bin/ (via rmarkdown)
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